Continue Test Network
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@@ -19,4 +19,4 @@ else()
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target_link_libraries(b_best Boost::boost "${XLSXWRITER_LIB}")
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endif()
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target_link_libraries(b_list ArffFiles mdlp "${TORCH_LIBRARIES}")
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target_link_libraries(testx ArffFiles mdlp "${TORCH_LIBRARIES}")
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target_link_libraries(testx ArffFiles mdlp BayesNet "${TORCH_LIBRARIES}")
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@@ -1,11 +1,16 @@
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#include "Folding.h"
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#include <torch/torch.h>
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#include "map"
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#include "Datasets.h"
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#include <map>
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#include <iostream>
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#include <sstream>
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#include "Datasets.h"
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#include "Network.h"
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#include "ArffFiles.h"
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#include "CPPFImdlp.h"
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using namespace std;
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using namespace platform;
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using namespace torch;
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string counts(vector<int> y, vector<int> indices)
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{
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@@ -21,45 +26,187 @@ string counts(vector<int> y, vector<int> indices)
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oss << endl;
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return oss.str();
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}
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class Paths {
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public:
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static string datasets()
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{
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return "datasets/";
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}
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};
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pair<vector<mdlp::labels_t>, map<string, int>> discretize(vector<mdlp::samples_t>& X, mdlp::labels_t& y, vector<string> features)
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{
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vector<mdlp::labels_t> Xd;
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map<string, int> maxes;
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auto fimdlp = mdlp::CPPFImdlp();
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for (int i = 0; i < X.size(); i++) {
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fimdlp.fit(X[i], y);
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mdlp::labels_t& xd = fimdlp.transform(X[i]);
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maxes[features[i]] = *max_element(xd.begin(), xd.end()) + 1;
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Xd.push_back(xd);
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}
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return { Xd, maxes };
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}
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vector<mdlp::labels_t> discretizeDataset(vector<mdlp::samples_t>& X, mdlp::labels_t& y)
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{
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vector<mdlp::labels_t> Xd;
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auto fimdlp = mdlp::CPPFImdlp();
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for (int i = 0; i < X.size(); i++) {
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fimdlp.fit(X[i], y);
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mdlp::labels_t& xd = fimdlp.transform(X[i]);
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Xd.push_back(xd);
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}
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return Xd;
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}
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bool file_exists(const string& name)
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{
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if (FILE* file = fopen(name.c_str(), "r")) {
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fclose(file);
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return true;
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} else {
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return false;
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}
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}
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tuple<Tensor, Tensor, vector<string>, string, map<string, vector<int>>> loadDataset(const string& name, bool class_last, bool discretize_dataset)
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{
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auto handler = ArffFiles();
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handler.load(Paths::datasets() + static_cast<string>(name) + ".arff", class_last);
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// Get Dataset X, y
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vector<mdlp::samples_t>& X = handler.getX();
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mdlp::labels_t& y = handler.getY();
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// Get className & Features
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auto className = handler.getClassName();
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vector<string> features;
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auto attributes = handler.getAttributes();
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transform(attributes.begin(), attributes.end(), back_inserter(features), [](const auto& pair) { return pair.first; });
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Tensor Xd;
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auto states = map<string, vector<int>>();
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if (discretize_dataset) {
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auto Xr = discretizeDataset(X, y);
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Xd = torch::zeros({ static_cast<int>(Xr.size()), static_cast<int>(Xr[0].size()) }, torch::kInt32);
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for (int i = 0; i < features.size(); ++i) {
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states[features[i]] = vector<int>(*max_element(Xr[i].begin(), Xr[i].end()) + 1);
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auto item = states.at(features[i]);
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iota(begin(item), end(item), 0);
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Xd.index_put_({ i, "..." }, torch::tensor(Xr[i], torch::kInt32));
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}
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states[className] = vector<int>(*max_element(y.begin(), y.end()) + 1);
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iota(begin(states.at(className)), end(states.at(className)), 0);
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} else {
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Xd = torch::zeros({ static_cast<int>(X.size()), static_cast<int>(X[0].size()) }, torch::kFloat32);
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for (int i = 0; i < features.size(); ++i) {
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Xd.index_put_({ i, "..." }, torch::tensor(X[i]));
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}
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}
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return { Xd, torch::tensor(y, torch::kInt32), features, className, states };
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}
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tuple<vector<vector<int>>, vector<int>, vector<string>, string, map<string, vector<int>>> loadFile(const string& name)
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{
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auto handler = ArffFiles();
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handler.load(Paths::datasets() + static_cast<string>(name) + ".arff");
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// Get Dataset X, y
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vector<mdlp::samples_t>& X = handler.getX();
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mdlp::labels_t& y = handler.getY();
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// Get className & Features
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auto className = handler.getClassName();
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vector<string> features;
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auto attributes = handler.getAttributes();
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transform(attributes.begin(), attributes.end(), back_inserter(features), [](const auto& pair) { return pair.first; });
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// Discretize Dataset
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vector<mdlp::labels_t> Xd;
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map<string, int> maxes;
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tie(Xd, maxes) = discretize(X, y, features);
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maxes[className] = *max_element(y.begin(), y.end()) + 1;
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map<string, vector<int>> states;
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for (auto feature : features) {
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states[feature] = vector<int>(maxes[feature]);
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}
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states[className] = vector<int>(maxes[className]);
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return { Xd, y, features, className, states };
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}
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class RawDatasets {
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public:
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RawDatasets(const string& file_name, bool discretize)
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{
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// Xt can be either discretized or not
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tie(Xt, yt, featurest, classNamet, statest) = loadDataset(file_name, true, discretize);
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// Xv is always discretized
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tie(Xv, yv, featuresv, classNamev, statesv) = loadFile(file_name);
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auto yresized = torch::transpose(yt.view({ yt.size(0), 1 }), 0, 1);
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dataset = torch::cat({ Xt, yresized }, 0);
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nSamples = dataset.size(1);
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weights = torch::full({ nSamples }, 1.0 / nSamples, torch::kDouble);
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weightsv = vector<double>(nSamples, 1.0 / nSamples);
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classNumStates = discretize ? statest.at(classNamet).size() : 0;
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}
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torch::Tensor Xt, yt, dataset, weights;
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vector<vector<int>> Xv;
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vector<double> weightsv;
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vector<int> yv;
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vector<string> featurest, featuresv;
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map<string, vector<int>> statest, statesv;
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string classNamet, classNamev;
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int nSamples, classNumStates;
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double epsilon = 1e-5;
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};
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int main()
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{
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map<string, string> balance = {
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{"iris", "33,33% (50) / 33,33% (50) / 33,33% (50)"},
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{"diabetes", "34,90% (268) / 65,10% (500)"},
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{"ecoli", "42,56% (143) / 22,92% (77) / 0,60% (2) / 0,60% (2) / 10,42% (35) / 5,95% (20) / 1,49% (5) / 15,48% (52)"},
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{"glass", "32,71% (70) / 7,94% (17) / 4,21% (9) / 35,51% (76) / 13,55% (29) / 6,07% (13)"}
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};
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for (const auto& file_name : { "iris", "glass", "ecoli", "diabetes" }) {
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auto dt = Datasets(true, "Arff");
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auto [X, y] = dt.getVectors(file_name);
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//auto fold = KFold(5, 150);
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auto fold = StratifiedKFold(5, y, -1);
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cout << "***********************************************************************************************" << endl;
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cout << "Dataset: " << file_name << endl;
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cout << "Nº Samples: " << dt.getNSamples(file_name) << endl;
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cout << "Class states: " << dt.getNClasses(file_name) << endl;
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cout << "Balance: " << balance.at(file_name) << endl;
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for (int i = 0; i < 5; ++i) {
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cout << "Fold: " << i << endl;
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auto [train, test] = fold.getFold(i);
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cout << "Train: ";
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cout << "(" << train.size() << "): ";
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// for (auto j = 0; j < static_cast<int>(train.size()); j++)
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// cout << train[j] << ", ";
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cout << endl;
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cout << "Train Statistics : " << counts(y, train);
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cout << "-------------------------------------------------------------------------------" << endl;
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cout << "Test: ";
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cout << "(" << test.size() << "): ";
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// for (auto j = 0; j < static_cast<int>(test.size()); j++)
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// cout << test[j] << ", ";
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cout << endl;
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cout << "Test Statistics: " << counts(y, test);
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cout << "==============================================================================" << endl;
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}
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cout << "***********************************************************************************************" << endl;
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// map<string, string> balance = {
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// {"iris", "33,33% (50) / 33,33% (50) / 33,33% (50)"},
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// {"diabetes", "34,90% (268) / 65,10% (500)"},
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// {"ecoli", "42,56% (143) / 22,92% (77) / 0,60% (2) / 0,60% (2) / 10,42% (35) / 5,95% (20) / 1,49% (5) / 15,48% (52)"},
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// {"glass", "32,71% (70) / 7,94% (17) / 4,21% (9) / 35,51% (76) / 13,55% (29) / 6,07% (13)"}
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// };
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// for (const auto& file_name : { "iris", "glass", "ecoli", "diabetes" }) {
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// auto dt = Datasets(true, "Arff");
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// auto [X, y] = dt.getVectors(file_name);
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// //auto fold = KFold(5, 150);
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// auto fold = StratifiedKFold(5, y, -1);
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// cout << "***********************************************************************************************" << endl;
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// cout << "Dataset: " << file_name << endl;
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// cout << "Nº Samples: " << dt.getNSamples(file_name) << endl;
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// cout << "Class states: " << dt.getNClasses(file_name) << endl;
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// cout << "Balance: " << balance.at(file_name) << endl;
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// for (int i = 0; i < 5; ++i) {
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// cout << "Fold: " << i << endl;
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// auto [train, test] = fold.getFold(i);
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// cout << "Train: ";
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// cout << "(" << train.size() << "): ";
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// // for (auto j = 0; j < static_cast<int>(train.size()); j++)
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// // cout << train[j] << ", ";
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// cout << endl;
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// cout << "Train Statistics : " << counts(y, train);
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// cout << "-------------------------------------------------------------------------------" << endl;
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// cout << "Test: ";
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// cout << "(" << test.size() << "): ";
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// // for (auto j = 0; j < static_cast<int>(test.size()); j++)
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// // cout << test[j] << ", ";
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// cout << endl;
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// cout << "Test Statistics: " << counts(y, test);
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// cout << "==============================================================================" << endl;
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// }
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// cout << "***********************************************************************************************" << endl;
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// }
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const string file_name = "iris";
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auto net = bayesnet::Network();
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auto dt = Datasets(true, "Arff");
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auto raw = RawDatasets("iris", true);
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auto [X, y] = dt.getVectors(file_name);
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cout << "Dataset dims " << raw.dataset.sizes() << endl;
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cout << "weights dims " << raw.weights.sizes() << endl;
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cout << "States dims " << raw.statest.size() << endl;
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cout << "features: ";
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for (const auto& feature : raw.featurest) {
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cout << feature << ", ";
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net.addNode(feature);
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}
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net.addNode(raw.classNamet);
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cout << endl;
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net.fit(raw.dataset, raw.weights, raw.featurest, raw.classNamet, raw.statest);
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}
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